The insufficient standardization of diagnostic next-generation sequencing (NGS) still limits its implementation in clinical practice, with the correct detection of mutations at low variant allele frequencies (VAF) facing particular challenges. We address here the standardization of sequencing coverage depth in order to minimize the probability of false positive and false negative results, the latter being underestimated in clinical NGS. There is currently no consensus on the minimum coverage depth, and so each laboratory has to set its own parameters. To assist laboratories with the determination of the minimum coverage parameters, we provide here a user-friendly coverage calculator. Using the sequencing error only, we recommend a minimum depth of coverage of 1,650 together with a threshold of at least 30 mutated reads for a targeted NGS mutation analysis of ≥3% VAF, based on the binomial probability distribution. Moreover, our calculator also allows adding assay-specific errors occurring during DNA processing and library preparation, thus calculating with an overall error of a specific NGS assay. The estimation of correct coverage depth is recommended as a starting point when assessing thresholds of NGS assay. Our study also points to the need for guidance regarding the minimum technical requirements, which based on our experience should include the limit of detection (LOD), overall NGS assay error, input, source and quality of DNA, coverage depth, number of variant supporting reads, and total number of target reads covering variant region. Further studies are needed to define the minimum technical requirements and its reporting in diagnostic NGS.

Millions of total joint replacements are performed annually worldwide, and the number is increasing every year. The overall proportion of patients achieving a successful outcome is about 80–90% in a 10–20-years time horizon postoperatively, periprosthetic osteolysis (PPOL) and aseptic loosening (AL) being the most frequent reasons for knee and hip implant failure and reoperations. The chemokine system (chemokine receptors and chemokines) is crucially involved in the inflammatory and osteolytic processes leading to PPOL/AL. Thus, the modulation of the interactions within the chemokine system may influence the extent of PPOL. Indeed, recent studies in murine models reported that (i) blocking the CCR2–CCL2 or CXCR2–CXCL2 axis or (ii) activation of the CXCR4–CXCL12 axis attenuate the osteolysis of artificial joints. Importantly, chemokines, inhibitory mutant chemokines, antagonists of chemokine receptors, or neutralizing antibodies to the chemokine system attached to or incorporated into the implant surface may influence the tissue responses and mitigate PPOL, thus increasing prosthesis longevity. This review summarizes the current state of the art of the knowledge of the chemokine system in human PPOL/AL. Furthermore, the potential for attenuating cell trafficking to the bone–implant interface and influencing tissue responses through modulation of the chemokine system is delineated. Additionally, the prospects of using immunoregenerative biomaterials (including chemokines) for the prevention of failed implants are discussed. Finally, this review highlights the need for a more sophisticated understanding of implant debris-induced changes in the chemokine system to mitigate this response effectively.

Sarcoidosis is an inflammatory granulomatous disease with unknown etiology driven by cytokines and chemokines. There is limited information regarding the regulation of cytokine/chemokine-receptor network in bronchoalveolar lavage (BAL) cells in pulmonary sarcoidosis, suggesting contribution of miRNAs and transcription factors. We therefore investigated gene expression of 25 inflammation-related miRNAs, 27 cytokines/chemokines/receptors, and a Th1-transcription factor T-bet in unseparated BAL cells obtained from 48 sarcoidosis patients and 14 control subjects using quantitative RT-PCR. We then examined both miRNA-mRNA expressions to enrich relevant relationships. This first study on miRNAs in sarcoid BAL cells detected deregulation of miR-146a, miR-150, miR-202, miR-204, and miR-222 expression comparing to controls. Subanalysis revealed higher number of miR-155, let-7c transcripts in progressing (n = 20) comparing to regressing (n = 28) disease as assessed by 2-year follow-up. Correlation network analysis revealed relationships between microRNAs, transcription factor T-bet, and deregulated cytokine/chemokine-receptor network in sarcoid BAL cells. Furthermore, T-bet showed more pronounced regulatory capability to sarcoidosis-associated cytokines/chemokines/receptors than miRNAs, which may function rather as “fine-tuners” of cytokine/chemokine expression. Our correlation network study implies contribution of both microRNAs and Th1-transcription factor T-bet to the regulation of cytokine/chemokine-receptor network in BAL cells in sarcoidosis. Functional studies are needed to confirm biological relevance of the obtained relationships.

Objective Aseptic loosening (AL) is the most frequent long-term reason for revision of total knee arthroplasty (TKA) affecting about 15–20% patients within 20 years after the surgery. Although there is a solid body of evidence about the crucial role of inflammation in the AL pathogenesis, scared information on inflammation signature and its time-axis in tissues around TKA exists. Design The inflammation protein signatures in pseudosynovial tissues collected at revision surgery from patients with AL (AL, n = 12) and those with no clinical/radiographic signs of AL (non-AL, n = 9) were investigated by Proximity Extension Assay (PEA)-Immunoassay and immunohistochemistry. Results AL tissues had elevated levels of TNF-family members sTNFR2, TNFSF14, sFasL, sBAFF, cytokines/chemokines IL8, CCL2, IL1RA/IL36, sIL6R, and growth factors sAREG, CSF1, comparing to non-AL. High interindividual variability in protein levels was evident particularly in non-AL. Levels of sTNFR2, sBAFF, IL8, sIL6R, and MPO discriminated between AL and non-AL and were associated with the time from index surgery, suggesting the cumulative character of inflammatory osteolytic response to prosthetic byproducts. The source of elevated inflammatory molecules was macrophages and multinucleated osteoclast-like cells in AL and histiocytes and osteoclast-like cells in non-AL tissues, respectively. All proteins were present in higher levels in osteoclast-like cells than in macrophages. Conclusions Our study revealed a differential inflammation signature between AL and non-AL stages of TKA. It also highlighted the unique patient’s response to TKA in non-AL stages. Further confirmation of our preliminary results on a larger cohort is needed. Analysis of the time-axis of processes ongoing around TKA implantation may help to understand the mechanisms driving periprosthetic bone resorption needed for diagnostic/preventative strategies.